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Knowledge discovery from area-class resource maps: capturing prototype effects / F. Qi in Cartography and Geographic Information Science, vol 35 n° 4 (October 2008)  

Public [article]  inCartography and Geographic Information Science > vol 35 n° 4 (October 2008) . - pp 223 - 237 Titre : Knowledge discovery from area-class resource maps: capturing prototype effects Type de document : Article/Communication Auteurs : F. Qi, Auteur ; A - Xing Zhu, Auteur ; Tao Pei, Auteur ; et al., Auteur Année de publication : 2008 Article en page(s) : pp 223 - 237 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] classification à base de connaissances [Termes IGN] exploration de données [Termes IGN] extraction de données [Termes IGN] objet géographique [Termes IGN] outil de découverte de connaissances Résumé : (Auteur) This paper presents a knowledge discovery approach to extracting knowledge from area-class resource maps. Prototype theory forms the basis of the approach which consists of two major components: (1) a scheme for organizing knowledge used in categorizing geographic entities which allows for the modeling of indeterminate boundaries and non-uniform memberships within categories; and (2) a data mining method using the Expectation Maximization (EM) algorithm for extracting such knowledge from area-class maps. A case study on knowledge discovery from a soil map demonstrates the details of the approach. The study shows that knowledge for classifying geographic entities with indeterminate boundaries is embedded in area-class maps and can be extracted through data mining; and that continuous spatial variation of geographic entities can be better modeled if the knowledge discovery process retains knowledge of within-class variations as well as transitions between classes. Copyright CaGISociety Numéro de notice : A2008-437 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1559/152304008786140533 En ligne : https://doi.org/10.1559/152304008786140533 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29506 [article]

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Knowledge discovery from soil maps using inductive learning / F. Qi in International journal of geographical information science IJGIS, vol 17 n° 8 (december 2003)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 17 n° 8 (december 2003) . - pp 771 - 795 Titre : Knowledge discovery from soil maps using inductive learning Type de document : Article/Communication Auteurs : F. Qi, Auteur ; A - Xing Zhu, Auteur Année de publication : 2003 Article en page(s) : pp 771 - 795 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] apprentissage dirigé [Termes IGN] arbre de décision [Termes IGN] carte pédologique [Termes IGN] cartographie géologique [Termes IGN] découverte de connaissances [Termes IGN] échantillonnage d'image [Termes IGN] filtrage du bruit [Termes IGN] histogramme [Termes IGN] intelligence artificielle [Termes IGN] réseau neuronal artificiel [Termes IGN] restauration d'image Résumé : (Auteur) This paper develops a knowledge discovery procedure for extracting knowledge of soil-landscape models from a soil map. It has broad relevance to knowledge discovery from other natural resource maps. The procedure consists of four major steps: data preparation, data preprocessing, pattern extraction, and knowledge consolidation. In order to recover true expert knowledge from the error-prone soil maps, our study pays specific attention to the reduction of representation noise in soil maps. The data preprocessing step has exhibited an important role in obtaining greater accuracy. A specific method for sampling pixels based on modes of environmental histograms has proven to be effective in terms of reducing noise and constructing representative sample sets. Three inductive learning algorithms, the See5 decision tree algorithm, Naïve Bayes, and artificial neural network, are investigated for a comparison concerning learning accuracy and result comprehensibility. See5 proves to be an accurate method and produces the most comprehensible results, which are consistent with the rules (expert knowledge) used in producing the soil map. The incorporation of spatial information into the knowledge discovery process is found not only to improve the accuracy of the extracted knowledge, but also to add to the explicitness and extensiveness of the extracted soil-landscape model. Numéro de notice : A2003-299 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658810310001596049 En ligne : https://doi.org/10.1080/13658810310001596049 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22595 [article]

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